colour.characterisation Package

Module Contents

class colour.characterisation.RGB_SpectralSensitivities(name, data)[source]

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for a camera RGB spectral sensitivities.

Parameters:
  • name (unicode) – Camera name.
  • data (dict) – Camera RGB spectral sensitivities data.
red
green
blue
blue

Property for self.z attribute.

Returns:self.z
Return type:SpectralPowerDistribution

Warning

RGB_SpectralSensitivities.blue is read only.

green

Property for self.y attribute.

Returns:self.y
Return type:SpectralPowerDistribution

Warning

RGB_SpectralSensitivities.green is read only.

red

Property for self.x attribute.

Returns:self.x
Return type:SpectralPowerDistribution

Warning

RGB_SpectralSensitivities.red is read only.

class colour.characterisation.RGB_DisplayPrimaries(name, data)[source]

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for a RGB display (such as a CRT or LCD) primaries tri-spectral power distributions.

Parameters:
  • name (unicode) – RGB display name.
  • data (dict) – RGB display primaries tri-spectral power distributions data.
red
green
blue
blue

Property for self.z attribute.

Returns:self.z
Return type:SpectralPowerDistribution

Warning

RGB_DisplayPrimaries.blue is read only.

green

Property for self.y attribute.

Returns:self.y
Return type:SpectralPowerDistribution

Warning

RGB_DisplayPrimaries.green is read only.

red

Property for self.x attribute.

Returns:self.x
Return type:SpectralPowerDistribution

Warning

RGB_DisplayPrimaries.red is read only.

colour.characterisation.first_order_colour_fit(m_1, m_2)[source]

Performs a first order colour fit from given \(m_1\) colour array to \(m_2\) colour array. The resulting colour fitting matrix is computed using multiple linear regression.

The purpose of that object is for example the matching of two ColorChecker colour rendition charts together.

Parameters:
  • m_1 (array_like, (3, n)) – Test array \(m_1\) to fit onto array \(m_2\).
  • m_2 (array_like, (3, n)) – Reference array the array \(m_1\) will be colour fitted against.
Returns:

Colour fitting matrix.
Return type:

ndarray, (3, 3)

Examples

>>> m_1 = np.array([
...     [0.17224810, 0.09170660, 0.06416938],
...     [0.49189645, 0.27802050, 0.21923399],
...     [0.10999751, 0.18658946, 0.29938611],
...     [0.11666120, 0.14327905, 0.05713804],
...     [0.18988879, 0.18227649, 0.36056247],
...     [0.12501329, 0.42223442, 0.37027445],
...     [0.64785606, 0.22396782, 0.03365194],
...     [0.06761093, 0.11076896, 0.39779139],
...     [0.49101797, 0.09448929, 0.11623839],
...     [0.11622386, 0.04425753, 0.14469986],
...     [0.36867946, 0.44545230, 0.06028681],
...     [0.61632937, 0.32323906, 0.02437089],
...     [0.03016472, 0.06153243, 0.29014596],
...     [0.11103655, 0.30553067, 0.08149137],
...     [0.41162190, 0.05816656, 0.04845934],
...     [0.73339206, 0.53075188, 0.02475212],
...     [0.47347718, 0.08834792, 0.30310315],
...     [0.00000000, 0.25187016, 0.35062450],
...     [0.76809639, 0.78486240, 0.77808297],
...     [0.53822392, 0.54307997, 0.54710883],
...     [0.35458526, 0.35318419, 0.35524431],
...     [0.17976704, 0.18000531, 0.17991488],
...     [0.09351417, 0.09510603, 0.09675027],
...     [0.03405071, 0.03295077, 0.03702047]])
>>> m_2 = np.array([
...     [0.15579559, 0.09715755, 0.07514556],
...     [0.39113140, 0.25943419, 0.21266708],
...     [0.12824821, 0.18463570, 0.31508023],
...     [0.12028974, 0.13455659, 0.07408400],
...     [0.19368988, 0.21158946, 0.37955964],
...     [0.19957425, 0.36085439, 0.40678123],
...     [0.48896605, 0.20691688, 0.05816533],
...     [0.09775522, 0.16710693, 0.47147724],
...     [0.39358649, 0.12233400, 0.10526425],
...     [0.10780332, 0.07258529, 0.16151473],
...     [0.27502671, 0.34705454, 0.09728099],
...     [0.43980441, 0.26880559, 0.05430533],
...     [0.05887212, 0.11126272, 0.38552469],
...     [0.12705825, 0.25787860, 0.13566464],
...     [0.35612929, 0.07933258, 0.05118732],
...     [0.48131976, 0.42082843, 0.07120612],
...     [0.34665585, 0.15170714, 0.24969804],
...     [0.08261116, 0.24588716, 0.48707733],
...     [0.66054904, 0.65941137, 0.66376412],
...     [0.48051509, 0.47870296, 0.48230082],
...     [0.33045354, 0.32904184, 0.33228886],
...     [0.18001305, 0.17978567, 0.18004416],
...     [0.10283975, 0.10424680, 0.10384975],
...     [0.04742204, 0.04772203, 0.04914226]])
>>> first_order_colour_fit(m_1, m_2)  
array([[ 0.6982266...,  0.0307162...,  0.1621042...],
       [ 0.0689349...,  0.6757961...,  0.1643038...],
       [-0.0631495...,  0.0921247...,  0.9713415...]])